arXiv subjects

Simon J. L. Billinge

Publications and source records attributed to Simon J. L. Billinge.

At least 37 records · Page 2Linked to original sources

arXiv2022

Robustness test of the spacegroupMining model for determining space groups from atomic pair distribution function data

Machine learning models based on convolutional neural networks have been used for predicting space groups of crystal structures from their atomic pair distribution function (PDF). However, the PDFs used to train the model are calculated using a fixed set of parameters that reflect specific experimental conditions, and the accuracy of the model when given PDFs generated with different choices of these parameters is unknown. In this paper, we report that the results of the top-1 accuracy and top-6 accuracy are robust when applied to PDFs of different choices of experimental parameters $r_\text{max}$, $Q_\text{max}$, $Q_\text{damp}$ and atomic displacement parameters.

cond-mat.mtrl-sci↗

arXiv2022

Refining perovskite structures to pair distribution function data using collective Glazer modes as a basis

Structural modelling of octahedral tilts in perovskites is typically done using the symmetry constraints of the resulting space group. In most cases, this introduces more degrees of freedom than those strictly necessary to describe only the octahedral tilts. It can therefore be a challenge to disentangle the octahedral tilts from other structural distortions such as cation displacements and octahedral distortions. This paper reports on the development of constraints for modelling pure octahedral tilts and implemented the constraints in diffpy-CMI, a powerful package to analyse pair distribution function (PDF) data. The program allows features in the PDF that come from rigid tilts to be separated from non-rigid relaxations, provides an intuitive picture of the tilting, and as it has many fewer refinable variables than the unconstrained space-group fits, provides robust and stable refinements of the tilt components. It further demonstrates the use of the model on the canonical tilted perovskite CaTiO$_3$ which has a known Glazer tilt system $α^+ β^- β^-$. The Glazer model fits comparably to the corresponding space group model $Pnma$ below $r$ = 14 A and becomes progressively worse than the space group model at higher $r$ due to non-rigid distortions in the real material.

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arXiv2022

Real space texture and pole figure analysis using the three-dimensional pair distribution function (PDF) on a Platinum thin film

An approach is described for studying texture in nanostructured materials. The approach implements the real space texture PDF, txPDF, laid out in [Gong and Billinge (2018) arXiv:1805.10342 [cond-mat]]. It is demonstrated on a fiber textured polycrystalline Pt thin film. The approach uses 3D PDF methods to reconstruct the orientation distribution function (ODF) of the powder crystallites from a set of diffraction patterns taken at different tilt angles of the substrate with respect to the incident beam directly from the 3D PDF of the sample. A real space equivalent of the reciprocal space pole figure is defined in terms of interatomic vectors in the PDF and computed for various interatomic vectors in the Pt film. Further, it is shown how a valid isotropic PDF may be obtained from a weighted average over the tilt series, and the measurement conditions for the best approximant to the isotropic PDF from a single exposure, which for the case of the fiber textured film was in a nearly grazing incidence orientation of around 10 degrees. Finally, we describe an open source Python software package, Fourigui, that may be used to help in studies of texture from 3D reciprocal space data, and indeed for Fourier transforming and visualizing 3D PDF data in general.

cond-mat.mtrl-sci↗

arXiv2021

nmfMapping: a cloud-based web application for non-negative matrix factorization of powder diffraction and pair distribution function datasets

A cloud-hosted web-based software application, nmfMapping, for carrying out a nonnegative matrix factorization of a set of powder diffraction or atomic pair distribution function datasets is described. This app allows structure scientists to rapidly find trends in sets of related data such as from in situ and operando diffraction experiments. The application is easy to use and does not require any programming expertise. It is available at the pdfitc.org website.

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arXiv2021

Intrinsic local symmetry-breaking in nominally cubic paraelectric BaTiO3

Whereas low-temperature ferroelectrics have a well understood ordered spatial dipole arrangement, the fate of these dipoles in paraelectric phases remains poorly understood. Using density functional theory (DFT), we find that unlike the case in conventional non-polar ABO$_3$ compounds illustrated here for cubic BaZrO$_3$, the origin of the distribution of the B site off-centering in cubic paraelectric such as BaTiO$_3$ is an intrinsic, energy stabilizing symmetry breaking. Minimizing the internal energy E of a constrained cubic phase already reveals the formation of a distribution of intrinsic local displacements that (i) mimic the symmetries of the low temperature phases, while (ii) being the precursors of what finite temperature DFT Molecular Dynamics finds as thermal motifs. The implications of such symmetry breaking on the microscopic structures and anomalous properties in these kinds of PE materials are discussed.

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arXiv2021

Recent Advances and Applications of Deep Learning Methods in Materials Science

Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. Recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep-learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science. The application of DL methods in materials science presents an exciting avenue for future materials discovery and design.

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arXiv2021

Towards scanning nanostructure x-ray microscopy

We demonstrate spatial mapping of the local and nano-scale structure of thin film objects using spatially resolved PDF analysis of synchrotron x-ray diffraction data. This is demonstrated in a lab-on-chip combinatorial array of sample spots containing catalytically interesting nanoparticles deposited from liquid precursors using an ink-jet liquid handling system. We present a software implementation of the whole protocol including an approach for automated data acquisition and analyis using the atomic pair distribution function (PDF) method. The protocol software can handle semi-automated data reduction, normalization and modelling, with user-defined recipes generating a comprehensive collection of metadata and analysis results. By slicing the collection using included functions it was possible to build images of the 2D object containing using different quantities for contrast, allowing us to determine the spatial map relating to different aspects of the local structure on the array.

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arXiv2021

A fast two-stage algorithm for non-negative matrix factorization in streaming data

In this article, we study algorithms for nonnegative matrix factorization (NMF) in various applications involving streaming data. Utilizing the continual nature of the data, we develop a fast two-stage algorithm for highly efficient and accurate NMF. In the first stage, an alternating non-negative least squares (ANLS) framework is used, in combination with active set method with warm-start strategy for the solution of subproblems. In the second stage, an interior point method is adopted to accelerate the local convergence. The convergence of the proposed algorithm is proved. The new algorithm is compared with some existing algorithms in benchmark tests using both real-world data and synthetic data. The results demonstrate the advantage of our algorithm in finding high-precision solutions.

math.OC↗

arXiv2020

Validation of non-negative matrix factorization for assessment of atomic pair-distribution function (PDF) data in a real-time streaming context

We validate the use of matrix factorization for the automatic identification of relevant components from atomic pair distribution function (PDF) data. We also present a newly developed software infrastructure for analyzing the PDF data arriving in streaming manner. We then apply two matrix factorization techniques, Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF), to study simulated and experiment datasets in the context of in situ experiment.

cond-mat.mtrl-sci↗

arXiv2020

Understanding Electronic Peculiarities in Tetragonal FeSe as Local Structural Symmetry Breaking

Traditional band theory of perfect crystalline solids often uses as input the structure deduced from diffraction experiments; when modeled by the minimal unit cell this often produces a spatially averaged model. The present study illustrates that this is not always a safe practice unless one examines if the intrinsic bonding mechanism is capable of benefiting from the formation of a distribution of lower symmetry local environments that differ from the macroscopically averaged structure. This can happen either due to positional, or due to magnetic symmetry breaking. By removing the constraint of a small crystallographic cell, the energy minimization in the density functional theory finds atomic and spin symmetry breaking, not evident in conventional diffraction experiments but being found by local probes such as pair distribution function analysis. Here we report that large atomic and electronic anomalies in bulk tetragonal FeSe emerge from the existence of distributions of local positional and magnetic moment motifs. The found symmetry-broken motifs obtained by minimization of the internal energy represent what chemical bonding in tetragonal phase prefers as an intrinsic energy lowering static distortions. This explains observations of band renormalization, predicts orbital order and enhanced nematicity, and provides unprecedented close agreement with spectral function measured by photoemission and local atomic environment revealed by pair distribution function. While the symmetry-restricted strong correlation approach has been argued previously to be the exclusive theory needed for describing the main peculiarities of FeSe, we show here that the symmetry-broken mean-field approach addresses numerous aspects of the problem, provides intuitive insight into the electronic structure, and opens the door for large-scale mean-field calculations for similar d-electron quantum materials.

cond-mat.mtrl-sci↗

arXiv2020

Two-orbital degeneracy lifted state as a local precursor to a metal-insulator transition

The recent discovery of a local fluctuating t2g orbital-degeneracy-lifted (ODL) state in CuIr2S4 as a high temperature precursor to the metal-insulator transition (MIT) opens the door to a possible widespread presence of precursor states in scarcely studied high-temperature regimes of transition metal based quantum materials. Although in CuIr2S4 the ODL state comprises one orbital per Ir, there is no fundamental reason to exclude multi-orbital ODL states in general. The MgTi2O4 spinel exhibits a MIT on cooling at Ts ~250 K, accompanied by Ti t2g orbital ordering (OO) and spin dimerization with the average symmetry reducing to tetragonal. It shares with CuIr2S4 the pyrochlore transition metal sublattice with active t2g orbitals. This, together with its different orbital filling (t2g1 vs t2g5.5) make it a candidate for hosting a multi-orbital ODL precursor state. By combining x-ray and neutron pair distribution function analyses to track the evolution of the local atomic structure across the MIT we find that local tetragonality already exists in the metallic globally cubic phase at high temperature. Local distortions exist up to at least 500 K. Significantly, the high temperature local state is not continuously connected to the OO band insulator ground state, and so the transition cannot be characterized as a trivial order-disorder type. The shortest Ti-Ti spin singlet dimer bonds expand abruptly on warming across the transition but remain shorter than those seen in the cubic structure. These seemingly contradictory observations can be understood within the model of a local fluctuating two-orbital t2g ODL precursor state. The ODL state in MgTi2O4 has a correlation length of about 1 nm at high temperature. We discuss that this extended character of the local distortions is consistent with the two-orbital nature of the ODL state imposed by the charge filling and the bond charge repulsion.

cond-mat.mtrl-sci↗

arXiv2020

Complete Strain Mapping of Nanosheets of Tantalum Disulfide

Quasi-two-dimensional (quasi-2D) materials hold promise for future electronics because of their unique band structures that result in electronic and mechanical properties sensitive to crystal strains in all three dimensions. Quantifying crystal strain is a prerequisite to correlating it with the performance of the device, and calls for high resolution but spatially resolved rapid characterization methods. Here we show that using fly-scan nano X-ray diffraction we can accomplish a tensile strain sensitivity below 0.001% with a spatial resolution of better than 80 nm over a spatial extent of 100 $μ$m on quasi 2D flakes of 1T-TaS2. Coherent diffraction patterns were collected from a $\sim$ 100 nm thick sheet of 1T-TaS2 by scanning 12keV focused X-ray beam across and rotating the sample. We demonstrate that the strain distribution around micron and sub-micron sized 'bubbles' that are present in the sample may be reconstructed from these images. The experiments use state of the art synchrotron instrumentation, and will allow rapid and non-intrusive strain mapping of thin film samples and electronic devices based on quasi 2D materials.

cond-mat.mtrl-sci↗

arXiv2020

Structure-mining: screening structure models by automated fitting to the atomic pair distribution function over large numbers of models

A new approach is presented to obtain candidate structures from atomic pair distribution function (PDF) data in a highly automated way. It fetches, from web-based structural databases, all the structures meeting the experimenter's search criteria and performs structure refinements on them without human intervention. It supports both x-ray and neutron PDFs. Tests on various material systems show the effectiveness and robustness of the algorithm in finding the correct atomic crystal structure. It works on crystalline and nanocrystalline materials including complex oxide nanoparticles and nanowires, low-symmetry and locally distorted structures, and complicated doped and magnetic materials. This approach could greatly reduce the traditional structure searching work and enable the possibility of high-throughput real-time auto analysis PDF experiments in the future.

cond-mat.mtrl-sci↗

arXiv2020

sasPDF: pair distribution function analysis of nanoparticle assemblies from small-angle-scattering data

We extends pair distribution function (PDF) analysis into the small-angle scattering (SAS) regime and describe the data collection protocol for optimum data quality. We also present the PDFgetS3 software package that can be readily used to extract the PDF from small-angle scattering data. We then apply the sasPDF method to investigate structures of some representative nanoparticle assemblies (NPA) samples with different levels of structural order.

cond-mat.mtrl-sci↗

arXiv2019

Using a machine learning approach to determine the space group of a structure from the atomic pair distribution function (PDF)

We present a method for predicting the space group of a structure given a calculated or measured atomic pair distribution function (PDF) from that structure. The method utilizes machine learning models trained on more than 100,000 PDFs calculated from structures in the 45 most heavily represented space groups. In particular, we present a convolutional neural network (CNN) model which yields a promising result that it correctly identifies the space group among the top-6 estimates 91.9~\% of the time. The CNN model also successfully identifies space groups on 12 out of 15 experimental PDFs. We discuss interesting aspects of the failed estimates, which indicate that the CNN is failing in similar ways as conventional indexing algorithms applied to conventional powder diffraction data. This preliminary success of the CNN model shows the possibility of model-independent assessment of PDF data on a wide class of materials.

cond-mat.mtrl-sci↗

arXiv2019

Cluster-mining: An approach for determining core structures of metallic nanoparticles from atomic pair distribution function data

We present a novel approach for finding and evaluating structural models of small metallic nanoparticles. Rather than fitting a single model with many degrees of freedom, the approach algorithmically builds libraries of nanoparticle clusters from multiple structural motifs, and individually fits them to experimental PDFs. Each cluster-fit is highly constrained. The approach, called cluster-mining, returns all candidate structure models that are consistent with the data as measured by a goodness of fit. It is highly automated, easy to use, and yields models that are more physically realistic and result in better agreement to the data than models based on cubic close-packed crystallographic cores, often reported in the literature for metallic nanoparticles.

cond-mat.mtrl-sci↗

arXiv2019

Algorithm for distance list extraction from pair distribution functions

We present an algorithm to extract the distance list from atomic pair distribution functions (PDFs) in a highly automated way. The algorithm is constructed via curve fitting based on a Debye scattering equation model. Due to the non-convex nature of the resulting optimization problem, a number of techniques are developed to overcome various computational difficulties. A key ingredient is a new approach to obtain a reasonable initial guess based on the theoretical properties of the mathematical model. Tests on various nanostructured samples show the effectiveness of the initial guess and the accuracy and overall good performance of the extraction algorithm. This approach could be extended to any spectrum that is approximated as a sum of Gaussian functions.

cond-mat.mtrl-sci↗

arXiv2018

Size-dependent lattice dynamics of atomically precise cadmium selenide quantum dots

Material properties depend sensitively on the atomic arrangements and atomic bonding, but these are notoriously difficult to measure in nanosized atomic clusters due to the small size of the objects and the challenge of obtaining bulk samples of identical clusters. Here we have combined the recent ability to make gram quantities of identical semiconductor quantum-dot nanoparticles with the ability to measure lattice dynamics on small sample quantities of hydrogenated materials using high energy resolution inelastic x-ray scattering (HERIX), to measure the size-dependence of the phonon density of states (PDOS) in CdSe quantum dots. The fact that we have atomically precise structural models for these nanoparticles allows the calculation of the PDOS using Density Functional Theory (DFT), providing both experimental and theoretical confirmations of the important role that the inertia of the surface capping species plays on determining the lattice dynamics.

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